sync .ipynb/.py in pre-commit, add preamble in .py files

.pre-commit-config.yaml

@@ -64,21 +64,16 @@ repos:
 
   - repo: https://github.com/mwouts/jupytext
     rev: v1.16.4
-    # converts (--to ipynb) the .py to the .ipynb and preserves (--update) the assigned
-    # "clear" cells' ids from the nbstripout
     hooks:
      - id: jupytext
-       name: convert .py to .ipynb
-       args: [--to, ipynb, --update]
-       files: '^examples/(.*\.py)$'
-  - repo: https://github.com/mwouts/jupytext
-    rev: v1.16.4
-    # if user interacted with the .ipynb and changed the kernel name
-    hooks:
-     - id: jupytext
-       name: update .ipynb metadata
-       args: [--update-metadata, '{"kernelspec": {"display_name": "Python 3 (ipykernel)","language": "python","name": "python3"}}']
-       files: '^examples/(.*\.ipynb)$'
+       name: sync .py and .ipynb
+       args:
+         - --sync
+         - --update-metadata
+         - '{"kernelspec": {"display_name": "Python 3 (ipykernel)","language": "python","name": "python3"}}'
+         - --set-formats
+         - ipynb,py:percent
+       files: '^examples/(.*\.ipynb|.*\.py)$'
 ci:
   autofix_commit_msg: |
     [pre-commit] auto fixes from pre-commit hooks

examples/direct_reconstruction.ipynb

@@ -206,7 +206,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/direct_reconstruction.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # Direct Reconstruction of 2D golden angle radial data
 # Here we use the DirectReconstruction class to reconstruct images from ISMRMRD 2D radial data

examples/iterative_sense_reconstruction.ipynb

@@ -306,7 +306,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/iterative_sense_reconstruction.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # Iterative SENSE Reconstruction of 2D golden angle radial data
 # Here we use the IterativeSENSEReconstruction class to reconstruct images from ISMRMRD 2D radial data

examples/pulseq_2d_radial_golden_angle.ipynb

@@ -180,7 +180,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/pulseq_2d_radial_golden_angle.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # Reconstruction of 2D golden angle radial data from pulseq sequence
 # Here we manually do all steps of a direction reconstruction, i.e.

examples/qmri_sg_challenge_2024_t1.ipynb

@@ -328,7 +328,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/qmri_sg_challenge_2024_t1.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # QMRI Challenge ISMRM 2024 - $T_1$ mapping
 

examples/qmri_sg_challenge_2024_t2_star.ipynb

@@ -285,7 +285,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/qmri_sg_challenge_2024_t2_star.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # QMRI Challenge ISMRM 2024 - $T_2^*$ mapping
 

examples/regularized_iterative_sense_reconstruction.ipynb

@@ -376,7 +376,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/regularized_iterative_sense_reconstruction.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # Regularized Iterative SENSE Reconstruction of 2D golden angle radial data
 # Here we use the RegularizedIterativeSENSEReconstruction class to reconstruct images from ISMRMRD 2D radial data

examples/t1_mapping_with_grad_acq.ipynb

@@ -500,7 +500,8 @@
  ],
  "metadata": {
   "jupytext": {
-   "cell_metadata_filter": "-all"
+   "cell_metadata_filter": "-all",
+   "formats": "ipynb,py:percent"
   },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",

examples/t1_mapping_with_grad_acq.py

@@ -1,3 +1,19 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.4
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
 # %% [markdown]
 # # $T_1$ mapping from a continuous Golden radial acquisition